In performing analysis of various aspects of an enterprise (e.g., a business, an educational organization, or a government agency), data can be received in the form of a time series, which is a collection of data points over time. The time series can then be analyzed to identify performance-related aspects of the enterprise, or to perform forecasting into the future.
In certain environments, time series can exhibit seasonal effects. For example, time series data relating to retail sales can exhibit a year-end holiday shopping effect, which provides an explanation for stronger sales closer to the end of the year than at other time periods of the year. Another example of a seasonal effect is a quarterly seasonal effect that explains why, for example, the last month in a quarter can be stronger than the first two months in terms of sales. Conventionally, reliance has been made on expertise of human experts in identifying seasonal effects. However, reliance on human experts is subject to variations in analyses (due to differences in experiences, biases, and training of human experts), and thus may not produce reliable results.